E-sport is a phenomenon where competitors compete against each other in video games. E-sport has experienced enormous growth the last few years, and is being watched by millions worldwide. In games like Dota 2, Leauge of Legends and Counter-Strike: Global Offensive players can make a living playing professionally. Some countries also broadcasts these competitions on live television.

With the growth of e-sports the demand for good spectating systems increases. Spectators wish to be shown the most interesting events as they unfold. In such high speed environments it becomes increasingly difficult for humans to operate the spectating systems.

In this paper we wish to explore the possibility of automatically predicting posisitions in the game where interesting events will happen in the future. To accomplish this we will be using machine learning to predict where these events will occur a given time in the future. These positions can then be incorporated with a spectating system for a fully automatic experience.

Research using machine learning for timeseries predictions has shown promising results, especially Recurrent Neural Networks, and more specifically Long Short-Term Memory-models. There is a lot of research on the usage of machine learning in games, from classic ones like chess, to modern video games like Dota 2.

In this paper we have compared different machine learning techniques. Convolutional Neural Networks did not show any promise of accomplishing good predictions. Recurrent Neural Networks however showed promising results, with LSTM performing the best. The best model seems to be able to predict relatively accurately 20 timesteps in the future, with possibilities of predicting 40 timesteps in the future.